A Volumetric Method for Building Complex Models from Range Images

Brian Curless
Marc Levoy
Stanford University

A number of techniques have been developed for reconstructing surfaces by
integrating groups of aligned range images. A desirable set of properties
for such algorithms includes: incremental updating, representation of
directional uncertainty, the ability to fill gaps in the re-construction,
and robustness in the presence of outliers. Prior algorithms possess
subsets of these properties. In this paper, we present a volumetric method
for integrating range images that possesses all of these properties.

Our volumetric representation consists of a cumulative weighted signed
distance function. Working with one range image at a time, we first
scan-convert it to a distance function, then combine this with the data
already acquired using a simple additive scheme. To achieve space
efficiency, we employ a run-length encoding of the volume. To achieve time
efficiency, we resample the range image to align with the voxel grid and
traverse the range and voxel scanlines synchronously. We generate the
final manifold by extracting an isosurface from the volumetric grid. We
show that under certain assumptions, this isosurface is optimal in the
least squares sense. To fill gaps in the model, we tessellate over the
boundaries between regions seen to be empty and regions never observed.

Using this method, we are able to integrate a large number of range images
(as many as 70) yielding seamless, high-detail models of up to 2.6 million
triangles.